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Canonicalization provides an architecture-agnostic method for enforcing equivariance, with generalizations such as frame-averaging recently gaining prominence as a lightweight and flexible alternative to equivariant architectures.
Über die mächtigkeit der zusammenhängenden mengen
Urysohn, P · 1925
Earlier work this paper cites.
Über die abbildungen der dreidimensionalen sphäre auf die kugelfläche
Hopf, H · 1931
Earlier work this paper cites.
Drei sätze über die n-dimensionale euklidische sphäre
Borsuk, K · 1933
Earlier work this paper cites.
Algebraic topology
Hatcher, A · 2002
Earlier work this paper cites.
Learning representations of sets through optimized permutations
Zhang, Y., Hare, J. S., and Prügel-Bennett, A · 2019
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On the universality of rotation equivariant point cloud networks
Dym, N. and Maron, H · 2020
Earlier work this paper cites.
Fspool: Learning set representations with featurewise sort pooling
Zhang, Y., Hare, J. S., and Prügel-Bennett, A · 2020
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Earlier work this paper cites.
Provably strict generalisation benefit for invariance in kernel methods
Elesedy, B · 2021
Earlier work this paper cites.
Highly accurate protein structure prediction with alphafold
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Earlier work this paper cites.
Learning with invariances in random features and kernel models
Mei, S., Misiakiewicz, T., and Montanari, A · 2021
Earlier work this paper cites.
Frame averaging for invariant and equivariant network design
Puny, O., Atzmon, M., Ben-Hamu, H., Smith, E. J., Misra, I., Grover, A., and Lipman, Y · 2021
Earlier work this paper cites.
E(n) equivariant graph neural networks
Satorras, V. G., Hoogeboom, E., and Welling, M · 2021
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Scalars are universal: Gauge-equivariant machine learning, structured like classical physics
Villar, S., Hogg, D. W., Storey-Fisher, K., Yao, W., and Blum-Smith, B · 2021
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E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J. P., Kornbluth, M., Molinari, N., Smidt, T. E., and Kozinsky, B · 2022
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SE(3) equivariant graph neural networks with complete local frames
Du, W., Zhang, H., Du, Y., Meng, Q., Chen, W., Zheng, N., Shao, B., and Liu, T.-Y · 2022
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Understanding and extending subgraph gnns by rethinking their symmetries
Frasca, F., Bevilacqua, B., Bronstein, M., and Maron, H · 2022
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Learning probabilistic symmetrization for architecture agnostic equivariance
Kim, J., Nguyen, T. D., Suleymanzade, A., An, H., and Hong, S · 2023
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Polynomial-time algorithms for continuous metrics on atomic clouds of unordered points
Kurlin, V · 2023
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Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations
Liao, Y.-L., Wood, B., Das, A., and Smidt, T · 2023
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Equivariant adaptation of large pretrained models
Mondal, A. K., Panigrahi, S. S., Kaba, S., Rajeswar, S., and Ravanbakhsh, S · 2023
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Reducing SO(3) convolutions to SO(2) for efficient equivariant GNNs
Passaro, S. and Zitnick, C. L · 2023
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